We evaluated both approaches on the eight classification datasets used in [1]}. The datasets are: BBC sports articles (BBCSPORT) labeled as one of athletics, cricket, footbal, rugby, tennis;
tweets labeled with sentiments ‘positive’, ‘negative’, or ‘neutral’
(TWITTR); recipes labeled by their region of origin (RECIPE); of medical abstracts labeled
by different cardiovascular disease groups (OHSUMED); sentences from academic papers labeled
by publisher name (CLASSIC); amazon reviews labeled by product category (AMAZON); news dataset
labeled by the news topics (REUTER); news articles classified into 20 different categories (20NEWS).
We removed all the words in the SMART stop word list [2]}. Documents are represented as bag of words.
To speed up training, we removed words that appear very few times over all the documents of a dataset.
Concretely, in 20NEWS we reduce the dictionary size by removing words with a frequency less or equal to three.
In the OHSUMED and CLASSIC datasets we remove words with frequency one and the in REUTER dataset words with
frequency equal or less than two.
[Alexandros]Why you have different cut off frequencies per dataset?
As feature side-information we use the word2vec representation of the words which have a dimensionality of 300
[3]};
other possibilities include knowledge-based side-information, e.g. based on WordNet [4]}.
[Alexandros]What is the size of your word2vec representations.
In table REF  we give a description of the final datasets on which we experiment
including the number of classes (\(m\) ) and average number of unique words per document.
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